Distributed Multi-Sensor Control for Multi-Target Tracking With a Sparsity-Promoting Objective Function
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Bibliographic record
Abstract
A distributed multi-sensor control method is presented for multi-target tracking. The problem is formulated as auctioned partially observed Markov decision processes (auctioned POMDPs), which is a tractable approach to approximate the solutions in a distributed manner. To ensure adequate coverage of the multi-sensor system, a sparsity-promoting objective function is also designed to reduce overlapping sensing areas, balancing a tradeoff between the control reward and sensor coverage. Simulation results demonstrate that the proposed distributed method achieves comparable tracking performance to the state-of-art centralized approach. Furthermore, the proposed sparsity-promoting objective function outperforms the conventional Cauchy-Schwarz divergence (CSD) in discovery performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it